Analysis of Fractional Order-Adaptive Systems Represented by Error Model 1 Using a Fractional-Order Gradient Approach

Maibeth Sánchez-Rivero (), Manuel A. Duarte-Mermoud, Juan Carlos Travieso-Torres, Marcos E. Orchard and Gustavo Ceballos-Benavides
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Maibeth Sánchez-Rivero: Departamento de Ingeniería Eléctrica, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Av. Tupper 2007, Santiago 8370451, Región Metropolitana, Chile
Manuel A. Duarte-Mermoud: Facultad de Ingeniería y Arquitectura, Universidad Central de Chile, Av. Santa Isabel 1186, Santiago 8330601, Región Metropolitana, Chile
Juan Carlos Travieso-Torres: Departamento de Tecnologías Industriales, Facultad Tecnológica, Universidad de Santiago de Chile, Av. El Belloto 3735, Santiago 9170125, Región Metropolitana, Chile
Marcos E. Orchard: Departamento de Ingeniería Eléctrica, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Av. Tupper 2007, Santiago 8370451, Región Metropolitana, Chile
Gustavo Ceballos-Benavides: Facultad de Ingeniería y Arquitectura, Universidad Central de Chile, Av. Santa Isabel 1186, Santiago 8330601, Región Metropolitana, Chile

Mathematics, 2024, vol. 12, issue 20, 1-16

Abstract: In adaptive control, error models use system output error and adaptive laws to update controller parameters for control or identification tasks. Fractional-order calculus, involving non-integer-order derivatives and integrals, is increasingly important for modeling, estimation, and control due to its ability to generalize classical methods and offer improved robustness to disturbances. This paper addresses the gap in the literature where fractional-order gradient methods have not yet been extensively applied in identification and adaptive control schemes. We introduce a fractional-order error model with fractional-order gradient (FOEM1-FG), which integrates fractional gradient operators based on the Caputo fractional derivative. By using theoretical analysis and simulations, we confirm that FOEM1-FG maintains stability and ensures bounded output errors across a variety of input signals. Notably, the fractional gradient’s performance improves as the order, β , increases with β > 1 , leading to faster convergence. Compared to existing integer-order methods, the proposed approach provides a more flexible and efficient solution in adaptive identification and control schemes. Our results show that FOEM1-FG offers superior stability and convergence characteristics, contributing new insights to the field of fractional calculus in adaptive systems.

Keywords: fractional-order calculus (FOC); fractional-order adaptive control (FOAC); steepest descend gradient (SDG); fractional-order steepest descend gradient (FOSDG) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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